Dear list, i'm using the GAM function from mgcv package. I'm using this syntax:
model=gam(y~offset(x)+s(log1p(x1))+s(log1p(x2))+s(x3)+s(x4)+s(5),family=quasipoisson,data=data) and I'm sequentially dropping the single term with the highest non-significant p-value from the model and re-fitting until all term are significant. Now I have: model=gam(y~offset(x)+s(log1p(x1))+s(log1p(x2))+s(x5),family=quasipoisson,data=data) summary(model) Approximate significance of smooth terms: edf Ref.df F p-value s(log1p(x1)) 1.000 1.00 36.984 8.09e-08 *** s(log1p(x2)) 13.174 13.84 5.767 5.66e-07 *** s(x5) 8.807 8.98 3.600 0.00118 ** My question is: may I increase the k parameter for the variable x1 to avoid the 1 edf and the linear relationship in the plot result. I tried: model=gam(y~offset(x)+s(log1p(x1)*,* k=15)+s(log1p(x2))+s(x5),family=quasipoisson,data=data) and all variables still significant and I have a edf higher than 1 and a non-linear relationship in the plot result. If I increase the k parameter for one variable, should I increase it for the other variables?? Does the increase (or decrease) of the k parameter changes the interpretation of the results? I'm not sure to understand when I should change or not the k parameter... and of course I read the help page choose.k {mgcv}. Thanks a lot in advance Sam [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.